- Follow the Flow documentation to setup Flow, SUMO and RLlib in the system. More tutorials and examples are available in the main repository of Flow.
- Activate virtual environment for flow and run train.py and simulate.py scripts in /Training as "python simulate.py EXP_CONFIG" or "python train.py EXP_CONFIG"
- Pre-trained models are present in /ray_results. Use "tensorboard --logdir ~/ray_results" to view the training statistics.
Key Papers and Datasets are available on https://traffic-signal-control.github.io/
Flow is a computational framework for deep RL and control experiments for traffic microsimulation.
See our website for more information on the application of Flow to several mixed-autonomy traffic scenarios. Other results and videos are available as well.
If you have a bug, please report it. Otherwise, join the Flow Users group on Slack!
We welcome your contributions.
- Please report bugs and improvements by submitting GitHub issue.
- Submit your contributions using pull requests. Please use this template for your pull requests.
If you use Flow for academic research, you are highly encouraged to cite our paper:
C. Wu, A. Kreidieh, K. Parvate, E. Vinitsky, A. Bayen, "Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control," CoRR, vol. abs/1710.05465, 2017. [Online]. Available: https://arxiv.org/abs/1710.05465
If you use the benchmarks, you are highly encouraged to cite our paper:
Vinitsky, E., Kreidieh, A., Le Flem, L., Kheterpal, N., Jang, K., Wu, F., ... & Bayen, A. M, Benchmarks for reinforcement learning in mixed-autonomy traffic. In Conference on Robot Learning (pp. 399-409). Available: http://proceedings.mlr.press/v87/vinitsky18a.html
Flow is supported by the Mobile Sensing Lab at UC Berkeley and Amazon AWS Machine Learning research grants. The contributors are listed in Flow Team Page.